Face Recognition using a Cognitive Processing Model

Tepvorachai, G.; Papachristou, C.

doi:10.1109/ahs.2008.33

Cited by 3 publications

(2 citation statements)

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“…As In Face Recognition using a Cognitive Processing Model (Gorn Tepvorachai and Chris Papachristou) [7] . In the conventional eigenface method, the principle component analysis (PCA) algorithm associates the Eigenvectors with the changes in illumination.…”

Section: Relkated Workmentioning

confidence: 99%

Intelligent sensor for image control point of eigenface for face recognition

Touré

Zou

2010

2010 2nd International Conference on Signal Processing Systems

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Sensors for Image control point in Face recognition (FR) is one of the most active research areas in computer vision and pattern recognition. Its practical application includes forensic identification, access control and human computer interface. The research aimed at comparing an input face image against a database containing a set of face samples with known identity and identifying the subject to which the input face belongs .To attain these objectives, we have used several images combined by image registration to improve eigenface recognition. Sensor detection by head orientation for image control point of the training sets collected in a database is also discussed. The methodological approach used to analyze eigenface focused on three fundamental points; Generating Eigenfaces, Classification and Identification using Image processing toolbox to perform the matrix calculations. The results obtained showed that the performance of the proposed technique proved to be less affected by registration errors and the quality of image was successfully.

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Section: Relkated Workmentioning

confidence: 99%

Intelligent sensor for image control point of eigenface for face recognition

Touré

Zou

2010

2010 2nd International Conference on Signal Processing Systems

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“…As In Face Recognition using a Cognitive Processing Model (Tepvorachai and Papachristou) (2008). In the conventional eigenface method, the Principle Component Analysis (PCA) algorithm associates the Eigenvectors with the changes in illumination.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Sensor for Image Control Point of Eigenfaces for Face Recognition

Touré¹,

Zou²

2010

Journal of Computer Science

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Problem statement: The sensor for image control point in Face Recognition (FR) is one of the most active research areas in computer vision and pattern recognition. Its practical application includes forensic identification, access control and human computer interface. The task of a FR system is to compare an input face image against a database containing a set of face samples with known identity and identifying the subject to which the input face belongs. However, a straightforward implementation is difficult since faces exhibit significant variations in appearance due to acquisition, illuminations, pose and aging variations. This research contracted with several images combined through image registration offering the possibility of improving eigenface recognition. Sensor detection by head orientation for image control point of the training sets collected in a database was discussed. Approach: In fact, the aim of such a research consisted first, identification of the face recognition and the possibility of improving eigenface recognition. So the approach of eigenface focused on three fundamental points: generating eigenfaces, classification and identification and the method used image processing toolbox to perform the matrix calculations. Results: Observation showed that the performance of the proposed technique proved to be less affected by registration errors. Conclusion/Recommendations: We presented the intelligent sensor for face recognition using image control point of eigenfaces. It is important to note that many applications of face recognition do not require perfect identification, although most require a low false-positive rate. In searching a large database of faces, for example, it may be preferable to find a small set of likely matches to present to the user

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Cognitive Information Processing in Face Recognition

Tepvorachai

Papachristou

Wolff

et al. 2008

2008 IEEE National Aerospace and Electronics Conference

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In the conventional eigenface method, the princi-One of many important issues in face recognition is to seple component analysis (PCA) algorithm associates the Eigen lect efficient facial image representations. Among appearancevectors with the changes in illumination. In this paper, we based facial image representations, eigenfaces [5], [6], fisherpropose an improvement of facial image association for face f recognition using a cognitive processing model. This method faces [5], [7], and gaborfaces [8] have proved to be effective is based on the notion of multiple-phase associative memory. on large databases. Principle component analysis (PCA) [6], The Essex face database is used to verify our model for facial independent component analysis (ICA) [9], and linear disimage recognition and compare the results of face recognition criminant analysis (LDA) [7] perform statistical dimensional with conventional eigenface method. The simulation results show reduction to capture the de-cofrelation among stored images.that the proposed cognitive processing model approach results

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Face Recognition using a Cognitive Processing Model

Cited by 3 publications

References 20 publications

Intelligent sensor for image control point of eigenface for face recognition

Intelligent sensor for image control point of eigenface for face recognition

Intelligent Sensor for Image Control Point of Eigenfaces for Face Recognition

Cognitive Information Processing in Face Recognition

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